AmLaw 100 firm cuts hot document summary preparation time 60% with Aurora AI

In high-stakes litigation, AmLaw 100 firms face mounting pressure to rapidly surface and communicate the most critical documents — so-called 'hot documents' — to partners, clients, and leadership. Manual preparation of hot document summaries is labor-intensive: reviewers must read, synthesize, and format findings document by document, often under tight deadlines. For large matters involving hundreds of thousands of documents, this bottleneck compounds quickly. The result is delayed stakeholder reporting, reviewer fatigue, and inconsistent summary quality that undermines confidence in the review output at precisely the moments when clarity matters most.

The firm engaged Consilio to deploy two Aurora AI tools — Aurora AI Investigate and Aurora AI Summarize — integrated directly with Relativity Server in a secure, privately hosted environment. Aurora AI Investigate applied large language model-based analysis to identify and surface hot documents from the broader review population, replacing manual triage. Aurora AI Summarize then generated structured, consistent summaries of those flagged documents at scale. The private hosting model was deliberate: by running entirely within the firm's controlled infrastructure rather than a shared cloud, the team maintained strict confidentiality over privileged matter data while still capturing the efficiency gains of generative AI. The two tools operated as a combined workflow rather than standalone utilities.

The integrated Aurora AI workflow delivered a 60% reduction in summary preparation time, saving 50 to 60 hours per matter compared to the prior manual process. Key outcomes included:

• Faster stakeholder reporting: critical insights reached partners and clients significantly sooner in the matter lifecycle
• Improved consistency: AI-generated summaries followed a structured format, reducing variance across reviewers
• Reduced reviewer burden: document review teams experienced measurably lower workload pressure during intensive production phases

The time savings represent a meaningful reallocation of senior reviewer capacity — hours previously spent on mechanical summarization could be redirected toward higher-judgment legal analysis.

• Integrate, don't isolate: pairing investigative AI with automated summarization produces compounding efficiency gains that neither tool achieves independently — treat them as a workflow, not separate point solutions.
• Private hosting is a viable path for privileged matters: firms need not choose between GenAI capability and data security; on-premises or privately hosted deployments on platforms like Relativity Server can satisfy both.
• Consistency at scale is itself a strategic advantage: AI-generated summaries reduce the quality variance that comes with large reviewer teams, improving how findings are perceived by stakeholders.
• Measure per-matter impact: quantifying time saved per matter (not just aggregate) makes the business case for AI investment legible to firm leadership and clients alike.